DECtp: Calling Differential Gene Expression Between Cancer and Normal Samples by Integrating Tumor Purity Information

Area of Science:

• Genomics
• Bioinformatics
• Cancer Research

Background:

• Differential gene expression analysis is vital for cancer research, aiding in biomarker discovery.
• Clinical tumor samples are often mixtures of cancer and normal cells, yet purity is frequently overlooked in analyses.
• Ignoring tumor purity can reduce the power and introduce bias in identifying differentially expressed genes (DEGs).

Purpose of the Study:

• To develop a novel method, DECtp, for identifying DEGs that incorporates tumor purity information.
• To evaluate the performance of DECtp against existing methods using simulated and real cancer data.

Main Methods:

• Developed DECtp, a method integrating tumor purity into a generalized least square procedure with a Wald test.
• Compared DECtp with t-test and limma on nine simulated datasets with varying sample sizes and noise levels.
• Applied DECtp to 14 tumor types from The Cancer Genome Atlas (TCGA) and compared results with limma.

Main Results:

• DECtp consistently achieved the highest area under the curves (AUCs) in simulations, demonstrating the importance of purity information.
• In TCGA data, DECtp identified DEGs that were more sensitive, consistent, and biologically meaningful than those found by limma.
• The study identified novel DEGs with potential for further experimental validation.

Conclusions:

• Integrating tumor purity information is critical for accurate DEG calling in cancer research.
• DECtp offers a more powerful and reliable approach for identifying DEGs compared to traditional methods.
• DECtp enhances the discovery of diagnostic, prognostic, and therapeutic biomarkers for various cancers.